Improved speech emotion recognition with Mel frequency magnitude coefficient

Automatic speech emotion recognition using machine learning is a demanding research topic in the field of affective computing. Identifying the speech features for speech emotion recognition is a challenging issue as the feature needs to emphasize the information about emotion from the speech. Spectral features play a vital role in emotion recognition from speech signals. In this paper, two modifications are made in the extraction of Mel frequency cepstral coefficient, they are, using magnitude spectrum instead of energy spectrum and exclusion of discrete cosine transform and extract Mel Frequency Magnitude Coefficient. Mel frequency magnitude coefficient is the log of magnitude spectrum on a non-linear Mel scale frequency. Mel frequency magnitude coefficient and three conventional spectral features, Mel frequency cepstral coefficient, log frequency power coefficient and linear prediction cepstral coefficient are tested on Berlin, Ravdess, Savee, EMOVO, eNTERFACE and Urdu databases with multiclass support vector machine as the classifier. Mel frequency magnitude coefficient as a stand alone feature recognizes emotion with an accuracy of 81.50% for Berlin, 64.31% for Ravdess, 75.63% for Savee, 73.30% for EMOVO, 56.41% for eNTERFACE and 95.25% for Urdu databases. Mel frequency magnitude coefficient is found to be the better spectral feature for the identification of emotion from speech compared to the conventional features. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Automatic speech emotion recognition using machine learning is a challenging research topic, with Mel frequency magnitude coefficient being identified as a better spectral feature for emotion identification from speech compared to conventional features.